TOTAL VOLUME:
$61.7b
24H VOL:
$229,178,007
24H TRANSACTIONS:
600,069,390
OPEN INTEREST:
$1,336,753,738
577,196
Markets across
14,485
events
MATCHED EVENTS:
4,077
PLATFORM COVERAGE:
4
Polymarket:
50%
VS.
Kalshi:
50%
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
A men's college basketball game between Boston College Eagles and SMU Mustangs scheduled for February 21, 2026 at 4:00 PM ET. Markets cover moneyline (winner), multiple point spreads, and over/under totals across Kalshi and Polymarket platforms.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's moneyline market contains a logical contradiction where both SMU victory and Boston College victory resolve to Yes, making the market fundamentally unresolvable. Polymarket's moneyline is logically sound with mutually exclusive outcomes.Hero Tip:
Do not trade the Kalshi moneyline. Use Polymarket's moneyline market for winner determination. All spread and total markets across both platforms are logically consistent and can be safely traded.Critical Divergence Points:
Kalshi:
Moneyline market states: 'If SMU wins...resolves to Yes' AND 'If Boston College wins...resolves to Yes'. This creates a logical tautology where any game outcome produces Yes resolution, violating binary market structure.Polymarket:
Moneyline market states: 'If Boston College wins, resolve to Boston College Eagles' OR 'If SMU wins, resolve to SMU Mustangs'. Clear mutual exclusivity with 50-50 split only if game is canceled with no makeup.
Our PredictionHero Resolution Divergence Alerts (RDA) are there to help users identify potential differences across platforms. They do not replace or supersede the official rules and description of any prediction market. Users are solely responsible for reviewing and understanding the applicable rules and resolution criteria before placing any trade or bet. If you notice a potential inconsistency, discrepancy, or error in an alert, please report it to our team so we can review and improve the accuracy of our data.
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